Machine psychology: autonomous behavior, perceptual categorization and conditioning in a brain-based device.
Machine psychology: autonomous behavior, perceptual categorization and conditioning in a brain-based device.
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机器心理学：基于大脑的设备中的自主行为、感知分类和调节。
DOI：
10.1093/cercor/12.8.818
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发表时间：
2002
期刊：
影响因子：
3.7
通讯作者：
G. Edelman
中科院分区：
文献类型：
--
作者：
J. Krichmar;G. Edelman
在研究活体动物行为过程中的大脑活动时，不可能同时分析从分子事件到运动反应的所有控制水平。为了深入了解控制水平如何相互作用，我们使用基于大脑的真实世界设备进行了合成神经建模。我们在这里描述的设计和性能，这样的设备，指定达尔文七，这是由计算机模拟类似物的皮质和皮质下结构的指导。当设备在真实的环境中工作时，可以同时检查达尔文七号的所有神经结构。通过分析其在知觉分类和条件行为过程中的神经活动，揭示了不变物体识别、经验依赖性知觉分类、一阶和二阶条件反射的神经机制，以及不同学习速率对食欲和厌恶事件反应的影响。虽然个体达尔文七世的样本发展了类似的分类反应，依赖于对环境的探索和感觉运动适应，但每个样本都表现出高度个体化的突触强度变化模式。通过对神经解剖学和大规模神经动力学进行详尽的分析和操纵，这种基于大脑的设备为理解皮层相互作用提供了有价值的方法。这些设备还为智能机器的开发提供了基础，这些智能机器在其构造中遵循神经生物学而不是计算原理。
In studying brain activity during the behavior of living animals, it is not possible simultaneously to analyze all levels of control from molecular events to motor responses. To provide insights into how levels of control interact, we have carried out synthetic neural modeling using a brain-based real-world device. We describe here the design and performance of such a device, designated Darwin VII, which is guided by computer-simulated analogues of cortical and subcortical structures. All levels of Darwin VII's neural architecture can be examined simultaneously as the device behaves in a real environment. Analysis of its neural activity during perceptual categorization and conditioned behavior suggests neural mechanisms for invariant object recognition, experience-dependent perceptual categorization, first-order and second-order conditioning, and the effects of different learning rates on responses to appetitive and aversive events. While individual Darwin VII exemplars developed similar categorical responses that depended on exploration of the environment and sensorimotor adaptation, each showed highly individual patterns of changes in synaptic strengths. By allowing exhaustive analysis and manipulation of neuroanatomy and large-scale neural dynamics, such brain-based devices provide valuable heuristics for understanding cortical interactions. These devices also provide the groundwork for the development of intelligent machines that follow neurobiological rather than computational principles in their construction.
影响因子：
56.9
作者：
WILSON, MA;MCNAUGHTON, BL
通讯作者：
MCNAUGHTON, BL
影响因子：
56.9
作者：
Crair, MC;Gillespie, DC;Stryker, MP
通讯作者：
Stryker, MP
影响因子：
2.5
作者：
THEUNISSEN, FE;MILLER, JP
通讯作者：
MILLER, JP